ml-stat - npm Package Compare versions

Comparing version 1.1.0 to 1.2.0

array.js

@@ -26,5 +26,5 @@ 'use strict';
 exports.max = function max(values) {
-    var max = -Infinity;
+    var max = values[0];
     var l = values.length;
-    for (var i = 0; i < l; i++) {
+    for (var i = 1; i < l; i++) {
         if (values[i] > max) max = values[i];
@@ -41,5 +41,5 @@ }
 exports.min = function min(values) {
-    var min = Infinity;
+    var min = values[0];
     var l = values.length;
-    for (var i = 0; i < l; i++) {
+    for (var i = 1; i < l; i++) {
         if (values[i] < min) min = values[i];
@@ -56,6 +56,6 @@ }
 exports.minMax = function minMax(values) {
-    var min = Infinity;
-    var max = -Infinity;
+    var min = values[0];
+    var max = values[0];
     var l = values.length;
-    for (var i = 0; i < l; i++) {
+    for (var i = 1; i < l; i++) {
         if (values[i] < min) min = values[i];
@@ -146,3 +146,3 @@ if (values[i] > max) max = values[i];
     if (!alreadySorted) {
-        values = values.slice().sort(compareNumbers);
+        values = [].concat(values).sort(compareNumbers);
     }
@@ -203,3 +203,3 @@ var l = values.length;
     if (!alreadySorted) {
-        values = values.slice().sort(compareNumbers);
+        values = [].concat(values).sort(compareNumbers);
     }
@@ -253,7 +253,32 @@ var l = values.length;
 
+/**
+ * IEEE Transactions on biomedical engineering, vol. 52, no. 1, january 2005, p. 76-
+ * Calculate the standard deviation via the Median of the absolute deviation
+ *  The formula for the standard deviation only holds for Gaussian random variables.
+ * @returns {{mean: number, stdev: number}}
+ */
+exports.robustMeanAndStdev = function robustMeanAndStdev(y) {
+    var mean = 0, stdev = 0;
+    var length = y.length, i = 0;
+    for (i = 0; i < length; i++) {
+        mean += y[i];
+    }
+    mean /= length;
+    var averageDeviations = new Array(length);
+    for (i = 0; i < length; i++)
+        averageDeviations[i] = Math.abs(y[i] - mean);
+    averageDeviations.sort(compareNumbers);
+    if (length % 2 == 1) {
+        stdev = averageDeviations[(length - 1) / 2] / 0.6745;
+    } else {
+        stdev = 0.5 * (averageDeviations[length / 2] + averageDeviations[length / 2 - 1]) / 0.6745;
+    }
+
+    return {mean, stdev};
+};
+
 exports.quartiles = function quartiles(values, alreadySorted) {
     if (typeof(alreadySorted) === 'undefined') alreadySorted = false;
     if (!alreadySorted) {
-        values = values.slice();
-        values.sort(compareNumbers);
+        values = [].concat(values).sort(compareNumbers);
     }
@@ -435,3 +460,3 @@
     if (!inPlace)
-        result = values.slice();
+        result = [].concat(values);
 
@@ -438,0 +463,0 @@ var theMean = exports.mean(result), l = result.length;

History.md

@@ -0,1 +1,7 @@
+1.2.0 / 2016-06-16
+==================
+
+* add array.robustMeanAndStdev
+* add matrix.min, matrix.max and matrix.minMax
+
 1.1.0 / 2015-09-02
@@ -2,0 +8,0 @@ ==================

matrix.js

 'use strict';
+
 var arrayStat = require('./array');
 
-// https://github.com/accord-net/framework/blob/development/Sources/Accord.Statistics/Tools.cs
+function compareNumbers(a, b) {
+    return a - b;
+}
 
-function entropy(matrix, eps) {
+exports.max = function max(matrix) {
+    var max = -Infinity;
+    for (var i = 0; i < matrix.length; i++) {
+        for (var j = 0; j < matrix[i].length; j++) {
+            if (matrix[i][j] > max) max = matrix[i][j];
+        }
+    }
+    return max;
+};
+
+exports.min = function min(matrix) {
+    var min = Infinity;
+    for (var i = 0; i < matrix.length; i++) {
+        for (var j = 0; j < matrix[i].length; j++) {
+            if (matrix[i][j] < min) min = matrix[i][j];
+        }
+    }
+    return min;
+};
+
+exports.minMax = function minMax(matrix) {
+    var min = Infinity;
+    var max = -Infinity;
+    for (var i = 0; i < matrix.length; i++) {
+        for (var j = 0; j < matrix[i].length; j++) {
+            if (matrix[i][j] < min) min = matrix[i][j];
+            if (matrix[i][j] > max) max = matrix[i][j];
+        }
+    }
+    return {min, max};
+};
+
+exports.entropy = function entropy(matrix, eps) {
     if (typeof(eps) === 'undefined') {
@@ -19,5 +54,5 @@ eps = 0;
     return -sum;
-}
+};
 
-function mean(matrix, dimension) {
+exports.mean = function mean(matrix, dimension) {
     if (typeof(dimension) === 'undefined') {
@@ -63,6 +98,6 @@ dimension = 0;
     return theMean;
-}
+};
 
-function standardDeviation(matrix, means, unbiased) {
-    var vari = variance(matrix, means, unbiased), l = vari.length;
+exports.standardDeviation = function standardDeviation(matrix, means, unbiased) {
+    var vari = exports.variance(matrix, means, unbiased), l = vari.length;
     for (var i = 0; i < l; i++) {
@@ -72,9 +107,9 @@ vari[i] = Math.sqrt(vari[i]);
     return vari;
-}
+};
 
-function variance(matrix, means, unbiased) {
+exports.variance = function variance(matrix, means, unbiased) {
     if (typeof(unbiased) === 'undefined') {
         unbiased = true;
     }
-    means = means || mean(matrix);
+    means = means || exports.mean(matrix);
     var rows = matrix.length;
@@ -99,5 +134,5 @@ if (rows === 0) return [];
     return vari;
-}
+};
 
-function median(matrix) {
+exports.median = function median(matrix) {
     var rows = matrix.length, cols = matrix[0].length;
@@ -111,3 +146,3 @@ var medians = new Array(cols);
         }
-        data.sort();
+        data.sort(compareNumbers);
         var N = data.length;
@@ -121,5 +156,5 @@ if (N % 2 === 0) {
     return medians;
-}
+};
 
-function mode(matrix) {
+exports.mode = function mode(matrix) {
     var rows = matrix.length,
@@ -159,7 +194,7 @@ cols = matrix[0].length,
     return modes;
-}
+};
 
-function skewness(matrix, unbiased) {
+exports.skewness = function skewness(matrix, unbiased) {
     if (typeof(unbiased) === 'undefined') unbiased = true;
-    var means = mean(matrix);
+    var means = exports.mean(matrix);
     var n = matrix.length, l = means.length;
@@ -189,7 +224,7 @@ var skew = new Array(l);
     return skew;
-}
+};
 
-function kurtosis(matrix, unbiased) {
+exports.kurtosis = function kurtosis(matrix, unbiased) {
     if (typeof(unbiased) === 'undefined') unbiased = true;
-    var means = mean(matrix);
+    var means = exports.mean(matrix);
     var n = matrix.length, m = matrix[0].length;
@@ -219,7 +254,8 @@ var kurt = new Array(m);
     return kurt;
-}
+};
 
-function standardError(matrix) {
+exports.standardError = function standardError(matrix) {
     var samples = matrix.length;
-    var standardDeviations = standardDeviation(matrix), l = standardDeviations.length;
+    var standardDeviations = exports.standardDeviation(matrix)
+    var l = standardDeviations.length;
     var standardErrors = new Array(l);
@@ -232,9 +268,9 @@ var sqrtN = Math.sqrt(samples);
     return standardErrors;
-}
+};
 
-function covariance(matrix, dimension) {
-    return scatter(matrix, undefined, dimension);
-}
+exports.covariance = function covariance(matrix, dimension) {
+    return exports.scatter(matrix, undefined, dimension);
+};
 
-function scatter(matrix, divisor, dimension) {
+exports.scatter = function scatter(matrix, divisor, dimension) {
     if (typeof(dimension) === 'undefined') {
@@ -250,4 +286,4 @@ dimension = 0;
     }
-    var means = mean(matrix, dimension),
-        rows = matrix.length;
+    var means = exports.mean(matrix, dimension);
+    var rows = matrix.length;
     if (rows === 0) {
@@ -296,8 +332,8 @@ return [[]];
     return cov;
-}
+};
 
-function correlation(matrix) {
-    var means = mean(matrix),
-        standardDeviations = standardDeviation(matrix, true, means),
-        scores = zScores(matrix, means, standardDeviations),
+exports.correlation = function correlation(matrix) {
+    var means = exports.mean(matrix),
+        standardDeviations = exports.standardDeviation(matrix, true, means),
+        scores = exports.zScores(matrix, means, standardDeviations),
         rows = matrix.length,
@@ -323,12 +359,12 @@ cols = matrix[0].length,
     return cor;
-}
+};
 
-function zScores(matrix, means, standardDeviations) {
-    means = means || mean(matrix);
-    if (typeof(standardDeviations) === 'undefined') standardDeviations = standardDeviation(matrix, true, means);
-    return standardize(center(matrix, means, false), standardDeviations, true);
-}
+exports.zScores = function zScores(matrix, means, standardDeviations) {
+    means = means || exports.mean(matrix);
+    if (typeof(standardDeviations) === 'undefined') standardDeviations = exports.standardDeviation(matrix, true, means);
+    return exports.standardize(exports.center(matrix, means, false), standardDeviations, true);
+};
 
-function center(matrix, means, inPlace) {
-    means = means || mean(matrix);
+exports.center = function center(matrix, means, inPlace) {
+    means = means || exports.mean(matrix);
     var result = matrix,
@@ -352,6 +388,6 @@ l = matrix.length,
     return result;
-}
+};
 
-function standardize(matrix, standardDeviations, inPlace) {
-    if (typeof(standardDeviations) === 'undefined') standardDeviations = standardDeviation(matrix);
+exports.standardize = function standardize(matrix, standardDeviations, inPlace) {
+    if (typeof(standardDeviations) === 'undefined') standardDeviations = exports.standardDeviation(matrix);
     var result = matrix,
@@ -378,6 +414,6 @@ l = matrix.length,
     return result;
-}
+};
 
-function weightedVariance(matrix, weights) {
-    var means = mean(matrix);
+exports.weightedVariance = function weightedVariance(matrix, weights) {
+    var means = exports.mean(matrix);
     var rows = matrix.length;
@@ -405,5 +441,5 @@ if (rows === 0) return [];
     return vari;
-}
+};
 
-function weightedMean(matrix, weights, dimension) {
+exports.weightedMean = function weightedMean(matrix, weights, dimension) {
     if (typeof(dimension) === 'undefined') {
@@ -452,7 +488,7 @@ dimension = 0;
     return means;
-}
+};
 
-function weightedCovariance(matrix, weights, means, dimension) {
+exports.weightedCovariance = function weightedCovariance(matrix, weights, means, dimension) {
     dimension = dimension || 0;
-    means = means || weightedMean(matrix, weights, dimension);
+    means = means || exports.weightedMean(matrix, weights, dimension);
     var s1 = 0, s2 = 0;
@@ -464,8 +500,8 @@ for (var i = 0, ii = weights.length; i < ii; i++) {
     var factor = s1 / (s1 * s1 - s2);
-    return weightedScatter(matrix, weights, means, factor, dimension);
-}
+    return exports.weightedScatter(matrix, weights, means, factor, dimension);
+};
 
-function weightedScatter(matrix, weights, means, factor, dimension) {
+exports.weightedScatter = function weightedScatter(matrix, weights, means, factor, dimension) {
     dimension = dimension || 0;
-    means = means || weightedMean(matrix, weights, dimension);
+    means = means || exports.weightedMean(matrix, weights, dimension);
     if (typeof(factor) === 'undefined') {
@@ -516,24 +552,2 @@ factor = 1;
     return cov;
-}
-
-module.exports = {
-    entropy: entropy,
-    mean: mean,
-    standardDeviation: standardDeviation,
-    variance: variance,
-    median: median,
-    mode: mode,
-    skewness: skewness,
-    kurtosis: kurtosis,
-    standardError: standardError,
-    covariance: covariance,
-    scatter: scatter,
-    correlation: correlation,
-    zScores: zScores,
-    center: center,
-    standardize: standardize,
-    weightedVariance: weightedVariance,
-    weightedMean: weightedMean,
-    weightedCovariance: weightedCovariance,
-    weightedScatter: weightedScatter
 };

package.json

 {
   "name": "ml-stat",
-  "version": "1.1.0",
+  "version": "1.2.0",
   "description": "Functions for computing stats on arrays and matrices",
@@ -31,6 +31,6 @@ "main": "index.js",
   "devDependencies": {
-    "mocha-better-spec-reporter": "latest",
-    "mocha": "latest",
-    "should": "latest"
+    "mocha-better-spec-reporter": "^3.0.1",
+    "mocha": "^2.5.1",
+    "should": "^8.2.2"
   }
 }

New alerts

License Policy Violation

License

This package is not allowed per your license policy. Review the package's license to ensure compliance.

Found 1 instance in 1 package

Fixed alerts

License Policy Violation

License

This package is not allowed per your license policy. Review the package's license to ensure compliance.

Found 1 instance in 1 package

Improved metrics

Total package byte prevSize

30568

increased by6.64%

Lines of code

908

increased by3.89%

No dependency changes